QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications.

Area of Science:

• Radiology
• Medical Imaging
• Artificial Intelligence

Background:

• The COVID-19 pandemic has increased the use of computed tomography (CT) lung imaging for patient care and biomarker discovery.
• Quantitative CT imaging, utilizing AI, offers potential for assessing COVID-19 severity, differential diagnosis, and disease progression.

Purpose of the Study:

• To establish best practices for quantitative CT lung imaging in the context of COVID-19.
• To accelerate the development of robust quantitative imaging algorithms and tools for COVID-19 research.

Main Methods:

• Development of best practice guidelines by the RSNA QIBA CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee.
• Recommendations for CT image acquisition settings for contemporary scanners.
• Guidance on scientific publication reporting and open data sharing.

Main Results:

• A set of best practices for quantitative CT lung imaging for COVID-19 has been developed.
• Specific recommendations for CT image acquisition are provided.
• Emphasis is placed on standardized reporting and data contribution to open science.

Conclusions:

• Adherence to standardized quantitative CT imaging protocols is essential for reliable COVID-19 biomarker development.
• These guidelines aim to improve the consistency and comparability of quantitative CT data globally.
• Promoting open data sharing will further accelerate research and clinical application of quantitative CT for COVID-19.